Pathogen diversity in meta-population networks

The pathogen diversity means that multiple strains coexist, and widely exist in the biology systems. The new mutation of SARS-CoV-2 leading to worldwide pathogen diversity is a typical example. What are the main factors of inducing the pathogen diversity? Previous studies indicated the pathogen mutation is the most important reason for inducing the pathogen diversity. The traffic network and gene network are crucial in shaping the dynamics of pathogen contagion, while their roles for the pathogen diversity still lacking a theoretical study. To this end, we propose a reaction–diffusion process of pathogens with mutations on meta-population networks, which includes population movement and strain mutation. We extend the Microscopic Markov Chain Approach (MMCA) to describe the model. Traffic networks make pathogen diversity more likely to occur in cities with lower infection densities. The likelihood of pathogen diversity is low in cities with short effective distances in the traffic network. Star-type gene network is more likely to lead to pathogen diversity than lattice-type and chain-type gene networks. When pathogen localization is present, infection is localized to strains that are at the endpoints of the gene network. Both the increased probability of movement and mutation promote pathogen diversity. The results also show that the population tends to move to cities with short effective distances, resulting in the infection density is high. © 2022 Elsevier Ltd

Application research of '5G + Chinese medicine service' in the prevention and control of COVID-19 epidemic

During the regularized COVID-19 epidemic control, the regional TCM intelligent cloud system is extended to the isolated medical observation sites through 5G technology. Combined with the advantages of local Chinese medicine hospitals, TCM services such as TCM preparations and TCM remote diagnosis and treatment were provided to personnel at isolated medical observation sites. These services not only reduce the risk of COVID-19 infection, but also relieve the anxiety of those under isolation and medical observation. '5G + TCM services' gives full play to the important role of TCM in the prevention and control of epidemics and provides epidemic prevention and control solutions suitable for China. © 2022 IEEE.

Data-Driven Modeling and Analysis for COVID-19 Pandemic Hospital Beds Planning

The COVID-19 pandemic presents unprecedented challenges for the US healthcare system, and the critical care settings are heavily impacted by the pressures of caring for COVID-19 patients. However, hospital pandemic preparedness has been hampered by a lack of disease specific planning guidelines. In this paper, we proposed a holistic modeling and analysis approach, with a system dynamics model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization, to support the hospital planning decisions. Our model was trained using the public data from the JHU Coronavirus Resource Center and was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL and public data from the Florida Department of Health (FDOH). Various experiments were conducted to investigate different control measures and the variants of the virus and their impact on the disease transmission, and subsequently, the hospital planning needs. Our proposed approach can be tailored to a given hospital setting of interest and is also generalizable to other hospitals to tackle the pandemic planning challenge. Note to Practitioners-We proposed a holistic modeling and analysis approach to support hospital preparedness and resource planning during the COVID-19 pandemic. To capture the highly dynamic pandemic environment, we developed a numerical method to estimate R-0, the effective basic reproductive rate, and used the most recent estimated data series of daily R-0 to project the change in R-0 in a short-term forecast window. The prediction of the daily confirmed cases in that forecast window were then obtained based on recursively solving the system dynamics model, and was validated to be very close to the real confirmed cases from the public record. This data-driven approach allows us to gain a systematic understanding of the common trends across different states and regions, and to evaluate the effect of the control measures like the stay-at-home order and the impact of the virus variants on the disease transmission behavior. Furthermore, the dynamic prediction allows us to evaluate the hospital resource needs during different stages of the pandemic. The insights obtained through this effort shed light on the impact of interventions (e.g., vaccines and control measures) on the hospital preparedness to support appropriate hospital resource allocation.

Autoimmune diseases and vaccination against coronavirus disease 2019

There is not yet sufficient evidence that autoimmune disease (AID) increases susceptibility to coronavirus disease 2019 (COVID-19);however, AID can induce organ damage, cardiovascular and respiratory disease, and the use of multiple immunosuppressants in treatment may increase the risk of adverse outcomes following COVID-19 infection in patients with AID. Prevention strategies for COVID-19 should be prioritized in patients with AID, and COVID-19 vaccination may be a useful route. The benefits of COVID-19 vaccination in AID patients with stable disease activity outweigh the potential risks. The mRNA vaccine, inactivated vaccine and recombinant protein subunit vaccine are currently recommended for patients with AID, but recombinant adenoviral vector vaccines should be administered only when the benefits outweigh the risks in a comprehensive assessment, and live attenuated vaccines should be avoided. Most AID-therapeutic drugs have little effect on the immune response to COVID-19 vaccines and can be used normally during the vaccination period, while some drugs, such as methotrexate and rituximab, may reduce the immune response to COVID-19 vaccine and the timing of vaccination should be adjusted. This article discusses the necessity, safety and precautions of COVID-19 vaccination in patients with AID based on the existing clinical guidelines, expert consensus and literature studies.

Modeling of Critically Ill Patient Pathways to Support Intensive Care Delivery

The COVID-19 pandemic has exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. New methods and strategies to facilitate timely and accurate interventions are needed. A virtual counterpart (digital twin) to critically ill patients would allow bedside providers to visualize how the organ systems interact to cause a clinical effect, offering them the opportunity to evaluate the effect of a specific intervention on a virtual patient before exposing an actual patient to potential harm. This work aims at developing a digital simulation that models the clinical pathway of critically ill patients. Using the mixed-methods approach with the support of multiprofessional clinical experts, we first identify the causal and associative relationships between organ systems, medical conditions, clinical markers, and interventions. We record these relationships as structured expert rules, depict them in a directed acyclic graph (DAG) format, and store them in a graph database (Neo4j). These structured expert rules are subsequently utilized to drive a simulation application that enables users to simulate the state trajectory of critically ill patients over a given simulated time period to test the impact of different interventions on patient outcomes. This simulation model will be the engine driving a future digital twin prototype, which will be used as an educational tool for medical students, and as a bedside decision support tool to enable clinicians to make faster and more informed treatment decisions. IEEE

Queueing Information Management of Primary Care Delivery With E-Visits

The goal of this work is to investigate the system configuration and information management of primary care delivery with electronic visits (e-visits). We consider a medical institution employing primary care physicians and other clinicians that offer office visits (in-person) and e-visits (through secure messaging from patient portals), and where different queue-joining behaviors: denoted as the mixed strategy, the duplication strategy and the threshold strategy are adopted by flexible patients based on different system configurations. Different queueing models are developed to capture flexible patients' queue-joining behaviors according to queueing information provision. In particular, we develop the equilibrium behavior of a dual server system where state information is available for one of the servers and the flexible patient exhibits a utility-maximizing behavior, which extends the literature on the analysis of queueing systems with strategic customers. The duplication strategy with deletion offers the least expected waiting time for the patients, and the threshold strategy provides the next best performance which is superior to the mixed strategy, which demonstrates the value of information. IEEE

Hospital Beds Planning and Admission Control Policies for COVID-19 Pandemic: A Hybrid Computer Simulation Approach

Health care systems are at the front line to fight the COVID-19 pandemic. Emergent questions for each hospital are how many general ward and intensive care unit beds are needed, and additionally, how to optimally allocate these resources during demand surge to effectively save lives. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a hybrid computer simulation approach, with a system dynamic model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization and subsequently determine bed allocations. Two control policies, the type-dependent admission control policy and the early step-down policy, based on patient risk profiling, were proposed to lower the overall death rate of the patient population in need of intensive care. The model was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL. The allocation of hospital beds to low-risk and high-risk arrival patients to achieve the goal of reducing the death rate, while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge. © 2021 IEEE.

Liver metastases from colorectal cancer were more susceptible to COVID-19

Background: Coronavirus diseases 2019 (COVID-19) are severe and prevalent diseases all over the world, which have caused more than 10 million infections and more than half million deaths so far Angiotensin I-converting enzyme 2(ACE2) and transmembrane serine protease 2 (TMPRSS2) are the entry receptors that play crucial roles in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 Due to the high expression of ACE2/ TMPRSS2, and the immunosuppression caused by cancer and anti-cancer therapy, cancer patients are more likely to be infected with SARS-CoV-2 Previous studies have showed that the high expression of ACE2/TMPRSS2 in lung cancer, tongue cancer and colorectal cancer may be related to the high susceptibility of SARS-CoV-2 However, the susceptibility of liver metastases has not been elucidated Methods: We explored the Gene Expression Omnibus(GEO) database to mine the datasets of liver metastases from colorectal cancer with matched normal liver R package limma was used to analysis the datasets Paired t-test was used to compare the expression of ACE2 and TMPRSS2 between groups Results: GSE38174 dataset of 90 cases was enrolled to compare the expression levels of ACE2 and TMPRSS2 in liver metastases, matched liver Halo of metastases and normal liver The results showed that ACE2 expression in liver metastases was higher than that in liver Halo of metastases and normal liver (3 303±0 426, 2 328±0 228 and 2 522±0 173;metastases vs Halo, p=0 016;metastases vs normal, p=0 065;and Halo vs normal, p=0 272;Figure A) The expression of TMPRSS2 in liver metastases was significant higher than that in liver Halo or normal liver (3 006±0 258, 2 511±0 118 and 2 409±0 129;metastases vs Halo, p=0 075;metastases vs normal, p=0 027;and Halo vs normal, p=0 250;Figure B) Conclusion: Liver metastases have a high-expression of ACE2 and TMPRSS2, which may make patients more susceptible to SARS-CoV-2 infection In addition, liver metastases could serve as a target of SARS-CoV-2, casing liver damage and virus replication and transport However, as the first study on the genetic susceptibility of liver metastases to SARS-CoV-2 from public databases, our research needs to be further verified with real-world data. Abbreviations: ACE 2: angiotensin I-converting enzyme 2;COVID-19: coronavirus diseases 2019;GEO: Gene Expression Omnibus;SARSCoV- 2: severe acute respiratory syndrome coronavirus 2;TMPRSS2: transmembrane serine protease 2.

Factors influencing health behaviours during the coronavirus disease 2019 outbreak in China: an extended information-motivation-behaviour skills model.

OBJECTIVES: This study explored the factors influencing health behaviours during the coronavirus disease 2019 (COVID-19) outbreak in China. The impact of perceived stress and positive perception of interventions on health behaviours in China were assessed using the extended information-motivation-behaviour skills (IMB) model. STUDY DESIGN: Cross-sectional survey. METHODS: The Questionstar online survey tool was used to construct a structured questionnaire based on the IMB model. Between 14 and 22 February 2020, during the peak of COVID-19 epidemic in China, 2449 participants were recruited by snowball sampling on WeChat and Tencent QQ social media platforms in China. Data were collected through an online questionnaire, and structural equation modelling was performed to evaluate the extended IMB model. RESULTS: Health behaviours were assessed using a scoring system (total score range: 8-40); the average health behaviour score in this study was 34.62 ± 4.44. The term 'health risk stress' refers to the impact that perceived stress has on health, and this was experienced by 39.9% of participants. Only 35.9% of participants answered all seven questions on COVID-19 information correctly. The final model showed that information, motivation, behavioural skills, heath risk stress and positive perception of interventions had significant direct effects on health behaviours. Health behaviours were positively associated with the positive perception of interventions but negatively associated with health risk stress. Behavioural skills had the greatest impact on health behaviours. CONCLUSIONS: In the face of public health emergencies, the extended IMB model has been used as a theoretical framework to construct more effective interventions. The government should pay attention to publicity and guidance, strengthen positive interactions with the public and disclose relevant information in a timely manner to gain trust and to maintain the positive public perception of the interventions. In terms of health education, the government should focus on behavioural skills, promptly rectify ineffective prevention information and raise awareness about the disease to relieve stress and anxiety in the population.